A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data

Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. It is of great significance to identify drug-induced target-organ toxicity, especially the detailed pathological findings, which are crucial for toxicity assessment, in the early stage of drug development process. A large variety of studies have devoted to identify drug toxicity. However, most of them are limited to single organ or only binary toxicity. Here we proposed a novel multi-label learning model named Att-RethinkNet, for predicting drug-induced pathological findings targeted on liver and kidney based on toxicogenomics data. The Att-RethinkNet is equipped with a memory structure and can effectively use the label association information. Besides, attention mechanism is embedded to focus on the important features and obtain better feature presentation. Our Att-RethinkNet is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized. And more importantly, it predicts multiple pathological findings at the same time, instead of predicting each pathology separately as the previous model did. To demonstrate the effectiveness of the proposed model, we compared the proposed method with a series of state-of-the-arts methods. Our model shows competitive performance and can predict potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way. The implementation of the proposed method is available at https://github.com/RanSuLab/Drug-Toxicity-Prediction-MultiLabel.Author summary: Drug-induced toxicity damages the health and is one of the key factors causing drug withdrawal from the market. Hence, to fully assess drug-induced toxicity, it is important to predict the detailed pathological findings, which are also crucial for toxicity mechanism understanding. However, most of the existing toxicity studies only predict binary toxicity (the toxicity or non-toxicity) or only predict the toxicity targeting single organ. The pathological findings of multiple organs are not well explored. Here we show, through the proposed Att-RethinkNet, it is possible to predict drug-induced pathological findings on both liver and kidney. Our results suggest that the Att-RethinkNet predicts potential hepatotoxicity and nephrotoxicity in a more accurate and reliable way, and it is applicable in multiple organs and takes account the compound type, dose, and administration time, so it is more comprehensive and generalized than the existing methods. The accurate prediction of pathological findings on multiple organs may benefit drug development.